library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)

theme_cowplot2 <- function(...) {
  theme_cowplot(font_size = 12, ...) %+replace%
    theme(strip.background = element_blank(),
          plot.background = element_blank())
}
theme_set(theme_cowplot2())
coi <- params$cell_type
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster

1 Cluster markers

1.1 Major Ovarian.cancer.cell markers for cell assign

### load all data ---------------------------------
source("_src/global_vars.R")

# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_highqc.rds"))

myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")

plot_data_wrapper <- function(cluster_res) {
  cluster_res <- enquo(cluster_res)
  as_tibble(FetchData(seu_obj, myfeatures)) %>% 
    left_join(meta_tbl, by = "sample") %>% 
    rename(cluster = !!cluster_res) %>% 
    mutate(cluster = as.character(cluster),
           tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}

plot_data <- plot_data_wrapper(louvain_cluster)

WFDC2, CD24, CLDN3, KRT7, KRT8, KRT17, KRT18, KRT19, EPCAM, WT1, CLDN4, MSLN, FOLR1, MUC1

1.2 Subtype currated markers

helper_f <- function(x) ifelse(is.na(x), "", x)

markers_v6_super[[coi]] %>% 
  group_by(subtype) %>% 
  mutate(rank = row_number(gene)) %>% 
  spread(subtype, gene) %>% 
  mutate_all(.funs = helper_f) %>% 
  formattable::formattable()

rank	Cancer.cell.1	Cancer.cell.2	Cancer.cell.3	Cancer.cell.4	Cancer.cell.5	Cancer.cell.6	Ciliated.cell.1	Ciliated.cell.2	Ciliated.cell.3	Cycling.cancer.cell.1	Cycling.cancer.cell.2	doublet.Immune.cell
1	DAPL1	IGFBP3	CXCL10	COL1A1	CCND1	FTL	C20orf85	C5orf49	ATP5F1E	CENPF	ATAD2	CD52
2	FOLR1	KRT17	IFI6	COL1A2	CRISP3	HSPA6	CAPS	CFAP126	DEFB1	HMGB2	H2AFZ	IGHG1
3	SCGB1D2	KRT7	IFIT1	COL3A1	OVGP1	MGP	CAPSL	DNALI1	GNAS	MKI67	HIST1H4C	IGKC
4	SCGB2A1	LCN2	IFIT2	DCN	PLCG2	RACK1	PIFO	PIFO	GPX3	PTTG1	PCLAF	IGLC3
5	SNHG19	MMP7	IFIT3	FN1	PTMS	SCX	TPPP3	RSPH1	IMPA2	TOP2A	TUBA1B	IL7R
6		S100A10	ISG15	SPARC	SNHG9	ZFAS1						PTPRC
7		S100A9	MX1
8		SLPI
9		TACSTD2

1.3 Subtype cluster markers

marker_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_highqc_markers_02.tsv")

## Hypergeometric test --------------------------------------

test_set <- marker_tbl %>% 
  group_by(cluster) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(k = length(cluster)) %>% 
  ungroup %>%
  select(cluster, gene, k) %>% 
  mutate(join_helper = 1) %>% 
  group_by(cluster, join_helper, k) %>%
  nest(test_set = gene)

markers_doub_tbl <- markers_v6 %>% 
  enframe("subtype", "gene") %>% 
  filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>% 
  unnest(gene) %>% 
  group_by(gene) %>% 
  filter(length(gene) == 1) %>% 
  mutate(subtype = paste0("doublet.", subtype)) %>% 
  bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))

ref_set <- markers_v6_super[[coi]] %>% 
  bind_rows(markers_doub_tbl) %>% 
  group_by(subtype) %>% 
  mutate(m = length(gene),
         n = length(rownames(seu_obj))-m,
         join_helper = 1) %>% 
  group_by(subtype, m, n, join_helper) %>%
  nest(ref_set = gene)

hyper_tbl <- test_set %>% 
  left_join(ref_set, by = "join_helper") %>% 
  group_by(cluster, subtype, m, n, k) %>%
  do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
  mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
  ungroup %>%
  mutate(qval = p.adjust(pval, "BH"),
         sig = qval < 0.01)

# hyper_tbl %>% 
#   group_by(subtype) %>% 
#   filter(any(qval < 0.01)) %>%
#   ggplot(aes(subtype, -log10(qval), fill = sig)) +
#   geom_bar(stat = "identity") +
#   facet_wrap(~cluster) +
#   coord_flip()
  
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
  
cluster_label_tbl <- hyper_tbl %>% 
  mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>% 
  arrange(qval, subtype) %>%
  group_by(cluster) %>% 
  slice(1) %>% 
  mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>% 
  select(cluster, cluster_label = subtype) %>% 
  ungroup %>% 
  mutate(cluster_label = make.unique(cluster_label, sep = "_"))

seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label

cluster_n_tbl <- seu_obj$cluster_label %>% 
  table() %>% 
  enframe("cluster_label", "cluster_n") %>% 
  mutate(cluster_nrel = cluster_n/sum(cluster_n))

marker_sheet <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  group_by(cluster_label) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(rank = row_number(-avg_logFC)) %>% 
  select(cluster_label, gene, rank) %>% 
  ungroup %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  spread(cluster_label, gene) %>% 
  mutate_all(.funs = helper_f)

marker_tbl_annotated <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  left_join(cluster_n_tbl, by = "cluster_label") %>% 
  select(-cluster) %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  arrange(cluster_label, -avg_logFC, p_val_adj)

write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_sheet.tsv"))

write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_table_annotated.tsv"))

formattable::formattable(marker_sheet)

rank	Cancer.cell.1	Cancer.cell.2	Cancer.cell.3	Cancer.cell.4	Cancer.cell.5	Cancer.cell.6	Cycling.cancer.cell.1	Cycling.cancer.cell.2	Ciliated.cell.1	Ciliated.cell.2	Ciliated.cell.3	doublet.Immune.cell
1	DAPL1	S100A9	ISG15	COL1A1	OVGP1	HSPA6	CENPF	HIST1H4C	CAPS	CFAP126	GPX3	IGKC
2	SCGB2A1	KRT17	CXCL10	COL3A1	PLCG2	FTL	PTTG1	TUBA1B	C20orf85	PIFO	GNAS	IGLC3
3	SCGB1D2	LCN2	IFIT3	COL1A2	CRISP3	RACK1	UBE2S	PCLAF	TPPP3	DNALI1	ATP5F1E	LTB
4	SNHG19	TACSTD2	IFIT1	FN1	SNHG9	ZFAS1	HMGB2	ATAD2	C9orf24	RSPH1	DEFB1	IGHG1
5	FOLR1	IGFBP3	MX1	SPARC	CCND1	SCX	TPX2	H2AFZ	RSPH1	C5orf49	IMPA2	IL7R
6	CRABP1	SLPI	IFI6	DCN	PTMS	MGP	ASPM	PCNA	C5orf49	LRRIQ1	GSTM3	CD52
7	PTGDS	S100A10	IFIT2	LUM	PEG10	IGFBP7	MKI67	CKS1B	TMEM190	FAM183A	PSMA7	PTPRC
8	THSD4	KRT7	IFI44L	IGFBP7	DSTN	NPM1	CCNB1	TK1	PIFO	C9orf24	ANXA4	GIMAP7
9	APOA1	RARRES1	PARP14	COL6A1	YWHAE	CNBP	CDC20	MKI67	FAM183A	CASC1	FTL	SRGN
10		GPRC5A	TNFSF10	COL6A2	SNHG19	FKBP4	TOP2A	TYMS	C1orf194	C20orf85	CAPS	IGHG3
11		MMP7	STAT1	CCDC80	APOLD1	EEF1D	CKS2	TUBB	CETN2	ARMC3	IGFBP7	IGLC2
12		CRYAB	IFI27	CTHRC1	MYL12B	CDV3	UBE2C	HMGB2	ODF3B	C1orf194	PCLAF	BTG1
13		FTH1	RSAD2	CALD1	PTPRA	GAPDH	ARL6IP1	DUT	TXN	TPPP3	PLIN2	CCR7
14		NDRG1	OAS1	VIM	WBP11	OAZ1	BIRC5	TOP2A	AGR3	CFAP45	PFDN4	KLF2
15		KRT19	XAF1	COL6A3	SNHG25	YBX3	LGALS1	DEK	CFAP126	EFCAB1	C12orf75	BIRC3
16		S100A11	GBP1	NNMT	RBMX	YBX1	CCNB2	SMC4	IGFBP7	DNAAF1	RHOBTB3	CD3D
17		VEGFA	SAMD9	SPARCL1	EPB41L2	LTC4S	TUBA1B	MCM7	CAPSL	ENKUR	CDKN2C	RGS1
18		ANXA1	IFIH1	LGALS1	RBM8A	CYC1	CEP55	UBE2C	C11orf88	CEP126	BMP7	SPP1
19		FTL	GBP4	MGP	PABPC4	NOP53	CDKN3	HELLS	MORN2	HYDIN	EIF3K	IL32
20		SLC2A1	PSMB9	TAGLN	METAP2	EEF2	NUSAP1	CDK1	TUBB4B	PPIL6	XAGE3	CD48
21		SOX4	DDX58	MMP11	AMD1	EDN1	JPT1	MCM3	TUBA1A	CAPSL	DSTN	FYB1
22		S100A6	LY6E	VCAN	STRAP	EIF4A2	H2AFZ	CENPF	LRRIQ1	ZMYND10	MSRB1	TRBC2
23		TFPI2	ISG20	ACTA2	BRD9	TSTA3	NMU	STMN1	FOXJ1	TEKT2	EIF1	IKZF1
24		PI3	OAS3	RARRES2	CREBZF	PRELID1	KPNA2	MYBL2	ZMYND10	CFAP43	ATP5PO	CORO1A
25		C15orf48	HLA-B	COL5A2	MYL6B	SLU7	HMMR	CLSPN	DNAAF1	DYDC2	RDH10	CD3G
26		NEAT1	CXCL11	AEBP1	PPT1	CANX	PTMS	UBE2T	C9orf116	CFAP44	COX6B1	B2M
27		S100A2	RNF213	POSTN	SNRPB	CTTNBP2	HMGN2	NASP	EFCAB1	CDC20B	SDC2	CD2
28		TNFRSF12A	LAP3	PLA2G2A	MYL12A	TTC1	ECT2	MCM4	EFHC1	CFAP54	SFTA2	GMFG
29		C19orf33	IFI16	ERP27	TMCO1	CLINT1	SMC4	SMC2	DNALI1	FAM81B	ORM1	RIPOR2
30		TCIM	TAP1	NBL1	SNRPB2	TMEM238	HMGB1	NUSAP1	DYDC2	SPATA17	CNTD2	ETS1
31		TAGLN	IDO1	COL8A1	NAP1L1	COX4I1	CENPE	HMGB1	IK	TEKT1	HOXB-AS3	GIMAP4
32		MAL2	HLA-A	TIMP3	ST13	ZNF787	STMN1	RAD51AP1	CEP126	SPEF1	PTH1R	HCST
33		LGALS3	PLSCR1	LRRC75A	PTX3	CLTB	ANP32E	ZWINT	SNTN	CFAP46	CYS1	TRAC
34		NUPR1	HERC5	IGFBP4	MIR4458HG	HIGD2A	CKS1B	TPX2	AL357093.2	DNAH7	GLRX	TRBC1
35		ANXA2	IFI44	CDH6	WDR70	NACA	KIF20B	DNMT1	ROPN1L	CFAP73	EEF1A2	TMSB4X
36		CST6	B2M	MMP2	WDR45B	UQCRB	RAD21	RRM2	MLF1	ZBBX	ERVMER34-1	CXCR4
37		EMP1	HLA-C	BGN	PA2G4	ATP5PB	NUCKS1	E2F1	PSENEN	ROPN1L	CA2	LAPTM5
38		IL32	OAS2	TPM1	NMT1	RSPO3	HMGB3	CENPW	ERICH3	EFCAB10	LINC02532	CD3E
39		SQSTM1	BST2	IGFBP6	TTYH1	RSL1D1	TROAP	RANBP1	CCDC170	WDR38	NCCRP1	C1QB
40		CAST	WARS	C1R	VIM	CYHR1	DLGAP5	MAD2L1	TEKT2	SPAG8	LAMB4	LCP1
41		MUC4	SAMD9L	SPON2	KRT81	UBE2D2	AURKA	TMPO	PERP	DRC1	BICC1	ARHGDIB
42		TGM2	EIF2AK2	COL5A1	IGFBP3	S100A1	H2AFV	CENPX	SPA17	CFAP157	MIOX	CD37
43		SAT1	UBE2L6	KRT7	NSA2	SRP72	TUBB4B	FANCI	C9orf135	RSPH4A	ORM2	TYROBP
44		CLDN4	MX2	HTRA1	HSP90B1	SSB	CENPA	TMEM106C	LRRC23	WDR49	CPNE8	CCL4
45		FAM3C	OASL	CAV1	HMGB1	PUF60	NCAPD2	DHFR	CFAP45	C7orf57	NID2	EMP3
46		BIRC3	IFI35	SYT8	TMA7	BTF3	CENPW	PRKDC	ARMC3	DYNLRB2	AC022784.1	LSP1
47		LDHA	IFITM3	C1S	ANO1	UQCRH	TUBA1C	HMGN2	WDR54	WDR63	SLCO4A1	IGHG4
48		CAMK2N1	HLA-E	SERPINF1	PEG3	EIF3E	NEK2	BIRC5	MNS1	MAP3K19	CR1L	TRAF3IP3
49		ITGB8	RARRES3	ANXA2	CCND2	MAP2K2	NUF2	DTYMK	TEKT1	C6orf118	RBP7	STK17B
50		MUC16	NUPR1	CFH	CD177	LINC02308	MAD2L1	ASPM	TUBA4B	DNAH12	PCAT19	ACAP1

2 Clusters

2.1 sizes

enframe(sort(table(seu_obj$cluster_label))) %>% 
  mutate(name = ordered(name, levels = rev(name))) %>% 
  ggplot() +
  geom_bar(aes(name, value), stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(y = c("#cells"), x = "cluster")

2.2 UMAP

alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)

common_layers_disc <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
  labs(color = "")
)

common_layers_cont <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  scale_color_gradientn(colors = viridis(9)),
  guides(color = guide_colorbar())
)

ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  #facet_wrap(~therapy) +
  ggtitle("Sub cluster")

3 Filtering out doublet and mito high clusters

3.1 UMAP

my_subtypes <- names(clrs$cluster_label[[coi]])

my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet")]

cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
# seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
# seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:20, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
# seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))

plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(cell_id = colnames(seu_obj_sub),
         cluster_label = ordered(cluster_label, levels = my_subtypes))
  
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}

if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  scale_color_manual(values = clrs$cluster_label[[coi]]) +
  #facet_wrap(~cluster_label) +
  ggtitle("Sub cluster")

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  # geom_point(aes(umapharmony_1, umapharmony_2), 
  #            color = "grey90", size = 0.01, 
  #            data = select(plot_data_sub, -tumor_supersite)) +
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

write_tsv(select(plot_data_sub, cell_id, everything(), -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))

3.2 add module scores and pathway scores

signature_modules <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>% 
  gather(module, gene) %>% 
  na.omit() %>% 
  group_by(module) %>% 
  do(gene = c(.$gene)) %>% 
  {setNames(.$gene, .$module)}

signature_modules$ISG.module <- c("CCL5", "CXCL10", "IFNA1", "IFNB1", "ISG15", "IFI27L2", "SAMD9L")

# ## compute expression module scores
# for (i in 1:length(signature_modules)) {
#   seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules[i], name = names(signature_modules)[i])
#   seu_obj_sub[[names(signature_modules)[i]]] <- seu_obj_sub[[paste0(names(signature_modules)[i], "1")]]
#   seu_obj_sub[[paste0(names(signature_modules)[i], "1")]] <- NULL
#   print(paste(names(signature_modules)[i], "DONE"))
# }
# 
# ## compute progeny scores
# progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>% 
#   as.matrix %>% 
#   progeny %>% 
#   as.data.frame %>% 
#   as.list
# 
# names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
# 
# for (i in 1:length(progeny_list)) {
#   seu_obj_sub <- AddMetaData(seu_obj_sub, metadata = progeny_list[[i]], 
#                              col.name = names(progeny_list)[i])
# }
# 
# write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))

3.3 marker heatmap

marker_top_tbl <- marker_sheet[,-1] %>% 
  slice(1:10) %>% 
  as.list %>% 
  .[!str_detect(names(.), "doublet")] %>% 
  enframe("cluster_label_x", "gene") %>% 
  unnest(gene)

plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>% 
  gather(gene, value, -c(1:(length(myfeatures)+1))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>% 
  group_by(cluster_label, gene) %>% 
  summarise(value = mean(value, na.rm = T)) %>% 
  group_by(gene) %>% 
  mutate(value = scales::rescale(value)) %>% 
  left_join(marker_top_tbl, by = "gene") %>% 
  mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))

ggplot(plot_data_markers) +
  geom_tile(aes(gene, cluster_label, fill = value)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  facet_grid(~cluster_label_x, scales = "free", space = "free") +
  scale_fill_gradientn(colors = viridis(9)) +
  labs(fill = "Scaled\nexpression") +
  theme(aspect.ratio = 1,
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank())

# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)

3.4 composition

3.4.1 per site

comp_site_tbl <- plot_data_sub %>%
  filter(!is.na(tumor_supersite)) %>% 
  group_by(cluster_label, tumor_supersite) %>%
  tally %>%
  group_by(tumor_supersite) %>%
  mutate(nrel = n/sum(n)*100) %>%
  ungroup

pnrel_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  scale_fill_manual(values = clrs$cluster_label[[coi]]) +
  labs(fill = "Cluster", y = "Fraction [%]", x = "")

pnabs_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, n, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  scale_fill_manual(values = clrs$cluster_label[[coi]]) +
  labs(fill = "Cluster", y = "# cells", x = "") 

plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")

# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)

3.4.2 per sample

comp_tbl_sample_sort <- plot_data_sub %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>% 
  tally %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>% 
  mutate(nrel = n/sum(n)*100,
         nsum = sum(n),
         log10n = log10(n),
         label_supersite = "Site",
         label_mutsig = "Signature",
         label_therapy = "Rx") %>% 
  ungroup %>% 
  arrange(desc(therapy), tumor_supersite) %>% 
  mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>% 
  mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
         tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>% 
  arrange(patient_id) %>% 
  mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))

sample_id_x_tbl <- plot_data_sub %>% 
  mutate(sort_short_x = cell_sort) %>% 
  distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>% 
  arrange(sample_id_x)

comp_tbl_sample_sort %>% 
  mutate(sort_short_x = cell_sort) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>% 
  select(sample_id_x, cluster_label, n, nrel, nsum) %>% 
  left_join(sample_id_x_tbl, by = "sample_id_x") %>% 
  write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))

ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)

max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]

comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
  if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
  if (y == "n") ylab <- paste0("Number\nof cells")
  p <- ggplot(comp_tbl_sample_sort, 
              aes_string("tumor_subsite_rx", y, fill = "cluster_label")) + 
    facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
    coord_cartesian(clip = "off") + 
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.x = element_blank(),
          axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5, 
                                      margin = margin(0, -0.4, 0, 0, unit = "npc")),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.line.x = element_blank(),
          strip.text.y = element_blank(),
          strip.text.x = element_blank(),
          strip.background.y = element_blank(),
          strip.background.x = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) + 
    labs(x = "",
         y = ylab) +
    guides(fill = FALSE)
  if (y == "nrel") p <- p + 
    geom_bar(stat = "identity") +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, 50, 100), 
                       labels = c("0", "50", "100"))
  if (y == "n") p <- p + 
    geom_bar(stat = "identity", position = position_stack()) +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, ymaxn/2, ymaxn),
                       limits = c(0, ymaxn),
                       labels = c("", ymaxn/2, ymaxn)) +
    expand_limits(y = c(0, ymaxn)) +
    theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
  return(p)
} 

common_label_layers <- list(
  geom_tile(color = "white", size = 0.15),
  theme(axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        axis.title.x = element_blank(),
        axis.line.x = element_blank(),
        strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc")),
  scale_y_discrete(expand = c(0, 0)),
  labs(y = ""),
  guides(fill = FALSE),
  facet_grid(~patient_id, 
             space = "free", scales = "free")
)

comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id), 
       aes(tumor_subsite_rx, label_supersite, 
           fill = tumor_supersite)) + 
  scale_fill_manual(values = clrs$tumor_supersite) +
  common_label_layers

comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_therapy, 
           fill = therapy)) + 
  scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
  common_label_layers

comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_mutsig, 
           fill = consensus_signature)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  common_label_layers

patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)

comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
  facet_grid(~patient_id, 
             space = "free", scales = "free") +
  coord_cartesian(clip = "off") + 
  theme_void() +
  theme(strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc"))

hist_plot_wrapper <- function(x = "nrel") {
  if (x == "nrel") {
    xlab <- paste0("Fraction of cells [%]")
    bw <- 5
  }
  if (x == "log10n") {
    xlab <- paste0("Number of cells")
    bw <- 0.2
  }
  p <- ggplot(comp_tbl_sample_sort) +
    ggridges::geom_density_ridges(
      aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
      stat = "binline", binwidth = bw, scale = 3) +
    facet_grid(label_supersite~., 
               space = "free", scales = "free") +
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.title.y = element_blank(),
          axis.line.y = element_blank(),
          strip.text = element_blank(),
          strip.background = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_discrete(expand = c(0, 0)) +
    guides(fill = FALSE) +
    labs(x = xlab)
  if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) + 
    scale_x_continuous(expand = c(0, 0), 
                       labels = function(x) parse(text = paste("10^", x)))
  return(p)
}

pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")

pcomp_grid <- plot_grid(comp_label_patient_id, 
                        pcomp1, pcomp2, 
                        comp_label_site, comp_label_rx, comp_label_mutsig,
                        ncol = 1, align = "v", 
                        rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))

phist1 <- hist_plot_wrapper("log10n")

pcomp_hist_grid <- ggdraw() +
  draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
  draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)

pcomp_hist_grid

# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)

3.4.3 site specific cluster enrichment

comp_tbl_z <- comp_tbl_sample_sort %>% 
  filter(therapy == "pre-Rx",
         !(tumor_supersite %in% c("Ascites", "Other"))) %>% 
  group_by(patient_id, cluster_label) %>% 
  arrange(patient_id, cluster_label, nrel) %>% 
  mutate(rank = row_number(nrel),
         z_rank = scales::rescale(rank)) %>% 
  mutate(mean_nrel = mean(nrel, na.rm = T),
         sd_nrel = sd(nrel, na.rm = T),
         z_nrel = (nrel - mean_nrel) / sd_nrel) %>% 
  ungroup()

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

4 session info

devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.2 (2019-12-12)
##  os       Debian GNU/Linux 10 (buster)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Etc/UTC                     
##  date     2021-01-20                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib
##  ape            5.3        2019-03-17 [2]
##  assertthat     0.2.1      2019-03-21 [2]
##  backports      1.1.10     2020-09-15 [1]
##  bibtex         0.4.2.2    2020-01-02 [2]
##  Biobase        2.46.0     2019-10-29 [2]
##  BiocGenerics   0.32.0     2019-10-29 [2]
##  bitops         1.0-6      2013-08-17 [2]
##  broom          0.7.2      2020-10-20 [1]
##  callr          3.4.2      2020-02-12 [1]
##  caTools        1.17.1.4   2020-01-13 [2]
##  cellranger     1.1.0      2016-07-27 [2]
##  cli            2.0.2      2020-02-28 [1]
##  cluster        2.1.0      2019-06-19 [3]
##  codetools      0.2-16     2018-12-24 [3]
##  colorblindr  * 0.1.0      2020-01-13 [2]
##  colorspace   * 1.4-2      2019-12-29 [2]
##  cowplot      * 1.0.0      2019-07-11 [2]
##  crayon         1.3.4      2017-09-16 [1]
##  data.table     1.12.8     2019-12-09 [2]
##  DBI            1.1.0      2019-12-15 [2]
##  dbplyr         2.0.0      2020-11-03 [1]
##  desc           1.2.0      2018-05-01 [2]
##  devtools       2.2.1      2019-09-24 [2]
##  digest         0.6.25     2020-02-23 [1]
##  dplyr        * 1.0.2      2020-08-18 [1]
##  ellipsis       0.3.1      2020-05-15 [1]
##  evaluate       0.14       2019-05-28 [2]
##  fansi          0.4.1      2020-01-08 [2]
##  farver         2.0.3      2020-01-16 [1]
##  fitdistrplus   1.0-14     2019-01-23 [2]
##  forcats      * 0.5.0      2020-03-01 [1]
##  formattable    0.2.0.1    2016-08-05 [1]
##  fs             1.5.0      2020-07-31 [1]
##  future         1.15.1     2019-11-25 [2]
##  future.apply   1.4.0      2020-01-07 [2]
##  gbRd           0.4-11     2012-10-01 [2]
##  gdata          2.18.0     2017-06-06 [2]
##  generics       0.0.2      2018-11-29 [2]
##  ggplot2      * 3.3.2      2020-06-19 [1]
##  ggrepel        0.8.1      2019-05-07 [2]
##  ggridges       0.5.2      2020-01-12 [2]
##  globals        0.12.5     2019-12-07 [2]
##  glue           1.3.2      2020-03-12 [1]
##  gplots         3.0.1.2    2020-01-11 [2]
##  gridExtra      2.3        2017-09-09 [2]
##  gtable         0.3.0      2019-03-25 [2]
##  gtools         3.8.1      2018-06-26 [2]
##  haven          2.3.1      2020-06-01 [1]
##  hms            0.5.3      2020-01-08 [1]
##  htmltools      0.4.0      2019-10-04 [2]
##  htmlwidgets    1.5.1      2019-10-08 [2]
##  httr           1.4.2      2020-07-20 [1]
##  ica            1.0-2      2018-05-24 [2]
##  igraph         1.2.5      2020-03-19 [1]
##  irlba          2.3.3      2019-02-05 [2]
##  jsonlite       1.7.1      2020-09-07 [1]
##  KernSmooth     2.23-16    2019-10-15 [3]
##  knitr          1.26       2019-11-12 [2]
##  labeling       0.3        2014-08-23 [2]
##  lattice        0.20-38    2018-11-04 [3]
##  lazyeval       0.2.2      2019-03-15 [2]
##  leiden         0.3.1      2019-07-23 [2]
##  lifecycle      0.2.0      2020-03-06 [1]
##  listenv        0.8.0      2019-12-05 [2]
##  lmtest         0.9-37     2019-04-30 [2]
##  lsei           1.2-0      2017-10-23 [2]
##  lubridate      1.7.9.2    2020-11-13 [1]
##  magrittr     * 2.0.1      2020-11-17 [1]
##  MASS           7.3-51.5   2019-12-20 [3]
##  Matrix         1.2-18     2019-11-27 [3]
##  memoise        1.1.0      2017-04-21 [2]
##  metap          1.2        2019-12-08 [2]
##  mnormt         1.5-5      2016-10-15 [2]
##  modelr         0.1.8      2020-05-19 [1]
##  multcomp       1.4-12     2020-01-10 [2]
##  multtest       2.42.0     2019-10-29 [2]
##  munsell        0.5.0      2018-06-12 [2]
##  mutoss         0.1-12     2017-12-04 [2]
##  mvtnorm        1.0-12     2020-01-09 [2]
##  nlme           3.1-143    2019-12-10 [3]
##  npsurv         0.4-0      2017-10-14 [2]
##  numDeriv       2016.8-1.1 2019-06-06 [2]
##  pbapply        1.4-2      2019-08-31 [2]
##  pillar         1.4.6      2020-07-10 [1]
##  pkgbuild       1.0.6      2019-10-09 [2]
##  pkgconfig      2.0.3      2019-09-22 [1]
##  pkgload        1.0.2      2018-10-29 [2]
##  plotly         4.9.1      2019-11-07 [2]
##  plotrix        3.7-7      2019-12-05 [2]
##  plyr           1.8.5      2019-12-10 [2]
##  png            0.1-7      2013-12-03 [2]
##  prettyunits    1.1.1      2020-01-24 [1]
##  processx       3.4.2      2020-02-09 [1]
##  progeny      * 1.11.3     2020-10-22 [1]
##  ps             1.3.2      2020-02-13 [1]
##  purrr        * 0.3.4      2020-04-17 [1]
##  R.methodsS3    1.7.1      2016-02-16 [2]
##  R.oo           1.23.0     2019-11-03 [2]
##  R.utils        2.9.2      2019-12-08 [2]
##  R6             2.4.1      2019-11-12 [1]
##  RANN           2.6.1      2019-01-08 [2]
##  rappdirs       0.3.1      2016-03-28 [2]
##  RColorBrewer   1.1-2      2014-12-07 [2]
##  Rcpp           1.0.4      2020-03-17 [1]
##  RcppAnnoy      0.0.16     2020-03-08 [1]
##  RcppParallel   4.4.4      2019-09-27 [2]
##  Rdpack         0.11-1     2019-12-14 [2]
##  readr        * 1.4.0      2020-10-05 [1]
##  readxl       * 1.3.1      2019-03-13 [2]
##  rematch        1.0.1      2016-04-21 [2]
##  remotes        2.1.0      2019-06-24 [2]
##  reprex         0.3.0      2019-05-16 [2]
##  reshape2       1.4.3      2017-12-11 [2]
##  reticulate     1.14       2019-12-17 [2]
##  rlang          0.4.8      2020-10-08 [1]
##  rmarkdown      2.0        2019-12-12 [2]
##  ROCR           1.0-7      2015-03-26 [2]
##  rprojroot      1.3-2      2018-01-03 [2]
##  rstudioapi     0.11       2020-02-07 [1]
##  rsvd           1.0.3      2020-02-17 [1]
##  Rtsne          0.15       2018-11-10 [2]
##  rvest          0.3.6      2020-07-25 [1]
##  sandwich       2.5-1      2019-04-06 [2]
##  scales         1.1.0      2019-11-18 [2]
##  sctransform    0.2.1      2019-12-17 [2]
##  SDMTools       1.1-221.2  2019-11-30 [2]
##  sessioninfo    1.1.1      2018-11-05 [2]
##  Seurat       * 3.1.2      2019-12-12 [2]
##  sn             1.5-4      2019-05-14 [2]
##  stringi        1.5.3      2020-09-09 [1]
##  stringr      * 1.4.0      2019-02-10 [1]
##  survival       3.1-8      2019-12-03 [3]
##  testthat       2.3.2      2020-03-02 [1]
##  TFisher        0.2.0      2018-03-21 [2]
##  TH.data        1.0-10     2019-01-21 [2]
##  tibble       * 3.0.4      2020-10-12 [1]
##  tidyr        * 1.1.2      2020-08-27 [1]
##  tidyselect     1.1.0      2020-05-11 [1]
##  tidyverse    * 1.3.0      2019-11-21 [2]
##  tsne           0.1-3      2016-07-15 [2]
##  usethis        1.5.1      2019-07-04 [2]
##  uwot           0.1.5      2019-12-04 [2]
##  vctrs          0.3.5      2020-11-17 [1]
##  viridis      * 0.5.1      2018-03-29 [2]
##  viridisLite  * 0.3.0      2018-02-01 [2]
##  withr          2.3.0      2020-09-22 [1]
##  xfun           0.12       2020-01-13 [2]
##  xml2           1.3.2      2020-04-23 [1]
##  yaml           2.2.1      2020-02-01 [1]
##  zoo            1.8-7      2020-01-10 [2]
##  source                                 
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## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library

MSK SPECTRUM freeze major subset deep dives

Florian Uhlitz

2021-01-20

1 Cluster markers

1.1 Major Ovarian.cancer.cell markers for cell assign

1.2 Subtype currated markers

1.3 Subtype cluster markers

2 Clusters

2.1 sizes

2.2 UMAP

3 Filtering out doublet and mito high clusters

3.1 UMAP

3.2 add module scores and pathway scores

3.3 marker heatmap

3.4 composition

3.4.1 per site

3.4.2 per sample

3.4.3 site specific cluster enrichment

4 session info